Balancing U-type assembly lines under uncertainty is addressed in this paper by formulating a robust problem and developing its optimization model and algorithm. U-type assembly layouts are shown to be more efficient than conventional straight lines. A great majority of studies on U-lines assume deterministic environments and ignore uncertainty in operation times. We aim to fill this research gap and, to the best of our knowledge, this study will be the first application of robust optimization to U-type assembly planning. We assume that the operation times are not fixed but they can vary. We employ robust optimization that considers worst case situations. To avoid over-pessimism, we consider that only a subset of operation times take their worst case values. To solve this problem, we suggest an iterative approximate solution algorithm. The efficiency of the algorithm is evaluated with some computational tests.